Margin losses can be interpreted as weighted convex surrogates of the zero-one loss, summed over all examples.
[1]}, [2]} proposed expressions for the expected value
and the variance of the AUC for a fixed error rate, in an attempt to maximize the AUC with different nonlinear algorithms.
[3]} incorporated unlabeled data to make an unsupervised AUC maximization algorithm.
Other algorithms have been proposed to obtain approximations of the global AUC value [4]}, [5]}, [6]}, [7]}.
[8]} proposed an active learning algorithm for computing a linear model that maximizes the AUC.
[9]} implemented an online learning algorithm that maximizes the AUC.
[10]} analyzed statistical consistency under class imbalance, using a mean of true positive and true negative rates.
[11]} studied calibration for margin-based losses for binary classification.
